Historical Determinants of Fintech Development : Evidence from Initial Coin Offerings

Peer reviewedPostprin

The legacy of wars around the world:Evidence from military directors

International audienceThis study estimates the effects of wars on countries and firms. We first show immediate negative effects of wars on economic and financial development as well as legal institutions. Using a cross-country sample of 93,697 firm-year observations, we further argue and show that (i) wars increase the supply of military directors in corporate boards; and(ii) military directors reduce firm performance as measured by Tobin’s Q and return on assets (ROA). We interpret these lingering effects as military directors possessing social capital but lacking business expertise. Our results are robust to a matched sample, a lagged difference model, a dynamic general method of moments model and to the control of country, industry and year fixed effects

Do bureaucratic checks improve firm value? Evidence from a natural experiment

Peer reviewedPublisher PD

Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy

As an emerging education strategy, learnersourcing offers the potential for personalized learning content creation, but also grapples with the challenge of predicting student performance due to inherent noise in student-generated data. While graph-based methods excel in capturing dense learner-question interactions, they falter in cold start scenarios, characterized by limited interactions, as seen when questions lack substantial learner responses. In response, we introduce an innovative strategy that synergizes the potential of integrating Signed Graph Neural Networks (SGNNs) and Large Language Model (LLM) embeddings. Our methodology employs a signed bipartite graph to comprehensively model student answers, complemented by a contrastive learning framework that enhances noise resilience. Furthermore, LLM's contribution lies in generating foundational question embeddings, proving especially advantageous in addressing cold start scenarios characterized by limited graph data interactions. Validation across five real-world datasets sourced from the PeerWise platform underscores our approach's effectiveness. Our method outperforms baselines, showcasing enhanced predictive accuracy and robustness

Deep Learning Reveals Key Immunosuppression Genes and Distinct Immunotypes in Periodontitis

Background: Periodontitis is a chronic immuno-inflammatory disease characterized by inflammatory destruction of tooth-supporting tissues. Its pathogenesis involves a dysregulated local host immune response that is ineffective in combating microbial challenges. An integrated investigation of genes involved in mediating immune response suppression in periodontitis, based on multiple studies, can reveal genes pivotal to periodontitis pathogenesis. Here, we aimed to apply a deep learning (DL)-based autoencoder (AE) for predicting immunosuppression genes involved in periodontitis by integrating multiples omics datasets. Methods: Two periodontitis-related GEO transcriptomic datasets (GSE16134 and GSE10334) and immunosuppression genes identified from DisGeNET and HisgAtlas were included. Immunosuppression genes related to periodontitis in GSE16134 were used as input to build an AE, to identify the top disease-representative immunosuppression gene features. Using K-means clustering and ANOVA, immune subtype labels were assigned to disease samples and a support vector machine (SVM) classifier was constructed. This classifier was applied to a validation set (Immunosuppression genes related to periodontitis in GSE10334) for predicting sample labels, evaluating the accuracy of the AE. In addition, differentially expressed genes (DEGs), signaling pathways, and transcription factors (TFs) involved in immunosuppression and periodontitis were determined with an array of bioinformatics analysis. Shared DEGs common to DEGs differentiating periodontitis from controls and those differentiating the immune subtypes were considered as the key immunosuppression genes in periodontitis. Results: We produced representative molecular features and identified two immune subtypes in periodontitis using an AE. Two subtypes were also predicted in the validation set with the SVM classifier. Three “master” immunosuppression genes, PECAM1, FCGR3A, and FOS were identified as candidates pivotal to immunosuppressive mechanisms in periodontitis. Six transcription factors, NFKB1, FOS, JUN, HIF1A, STAT5B, and STAT4, were identified as central to the TFs-DEGs interaction network. The two immune subtypes were distinct in terms of their regulating pathways. Conclusion: This study applied a DL-based AE for the first time to identify immune subtypes of periodontitis and pivotal immunosuppression genes that discriminated periodontitis from the healthy. Key signaling pathways and TF-target DEGs that putatively mediate immune suppression in periodontitis were identified. PECAM1, FCGR3A, and FOS emerged as high-value biomarkers and candidate therapeutic targets for periodontitis